Abstract April 24, 2019

Speaker: Ave Arellano, Hydrology & Atmospheric Sciences

Title: Data Assimilation and Inverse Modeling of the Atmospheric Composition

Abstract: The availability of measurements of chemical and aerosol constituents offers an opportunity to study changes in atmospheric composition through the integration of these measurements with predictions from regional to global chemical transport models. Central to this integration is a chemical data assimilation (DA) and/or inverse modeling system that is reasonably efficient, effective, and flexible in assimilating measurements spanning multiple spatiotemporal scales and multiple chemical/aerosol species. This lecture introduces the development and application of data assimilation and inverse modeling approaches in atmospheric chemistry and physics. I will present three key scientific/technical problems that this community is attempting to address with these DA approaches. These are: 1) estimating sources and sinks of trace gases and aerosols, 2) assimilating multi-species and/or multi-platform chemical data research and/or operational chemical weather forecasting. and 3) conducting observing system simulation experiments (or OSSEs) to support future satellite observations of global atmospheric composition. I will end this lecture by posing a question regarding the complementary roles of machine learning and data assimilation in atmospheric composition studies